Science Archaeology Medieval duke's remains recount his grisly murder In 1272, Hungary's Béla of Macsó received over 23 sword gashes-and more. Breakthroughs, discoveries, and DIY tips sent every weekday. In 1272 CE, a Hungarian duke was murdered in cold blood. Details surrounding the grisly killing of the 13th century Hungarian duke named Béla of Macsó have remained murky for centuries. The duke met his demise at the hand of enemies, but far less is known about what motivated his killers or how the attack really unfolded.

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John Schneider tells Fox News Digital that he isn't afraid of artificial intelligence because it can't replicate the "heart" or the "soul." "What AI does not have and what AI cannot simulate is a heart, is a soul. So, I'm not afraid of AI," he told Fox News Digital. Schneider gave an analogy, comparing the technology to artificial dairy coffee creamer, to explain why he's not concerned. "A lot of people are talking about AI like it's this terrible, terrible thing that's coming in. I think it's powdered cream at best," he said.

Large Language Models (LLMs), acting as a powerful reasoner and generator, exhibit extraordinary performance across various natural language tasks, such as question answering (QA). Among these tasks, Multi-Hop Question Answering (MHQA) stands as a widely discussed category, necessitating seamless integration between LLMs and the retrieval of external knowledge. Existing methods employ LLM to generate reasoning paths and plans, and utilize IR to iteratively retrieve related knowledge, but these approaches have inherent flaws. On one hand, Information Retriever (IR) is hindered by the low quality of generated queries by LLM. On the other hand, LLM is easily misguided by the irrelevant knowledge by IR. These inaccuracies, accumulated by the iterative interaction between IR and LLM, lead to a disaster in effectiveness at the end. To overcome above barriers, in this paper, we propose a novel pipeline for MHQA called Furthest-Reasoning-with-Plan-Assessment (FuRePA), including an improved framework (Furthest Reasoning) and an attached module (Plan Assessor). 1) Furthest reasoning operates by masking previous reasoning path and generated queries for LLM, encouraging LLM generating chain of thought from scratch in each iteration. This approach enables LLM to break the shackle built by previous misleading thoughts and queries (if any). 2) The Plan Assessor is a trained evaluator that selects an appropriate plan from a group of candidate plans proposed by LLM. Our methods are evaluated on three highly recognized public multi-hop question answering datasets and outperform state-of-the-art on most metrics (achieving a 10%-12% in answer accuracy).

During the last decade, we have witnessed an increase in the volume of video content that is disseminated over the Internet. The pandemic further exacerbated this trend as people started to consume a wide category of videos from their houses [1]. Along with lectures, we have also witnessed a rise in the conferences and talks that are being recorded and uploaded online on streaming sites. These videos augment the material taught in the classrooms and are increasingly being leveraged for educational purposes [2]. Educational videos, like entertainment videos, are consumed in a combination of personal devices such as laptops, tablets, smartphones, and studies.

The approaches by which the machine learning and clinical research communities utilize real world data (RWD), including data captured in the electronic health record (EHR), vary dramatically. While clinical researchers cautiously use RWD for clinical investigations, ML for healthcare teams consume public datasets with minimal scrutiny to develop new algorithms. This study bridges this gap by developing and validating ML-DQA, a data quality assurance framework grounded in RWD best practices. The ML-DQA framework is applied to five ML projects across two geographies, different medical conditions, and different cohorts. A total of 2,999 quality checks and 24 quality reports were generated on RWD gathered on 247,536 patients across the five projects. Five generalizable practices emerge: all projects used a similar method to group redundant data element representations; all projects used automated utilities to build diagnosis and medication data elements; all projects used a common library of rules-based transformations; all projects used a unified approach to assign data quality checks to data elements; and all projects used a similar approach to clinical adjudication. An average of 5.8 individuals, including clinicians, data scientists, and trainees, were involved in implementing ML-DQA for each project and an average of 23.4 data elements per project were either transformed or removed in response to ML-DQA. This study demonstrates the importance role of ML-DQA in healthcare projects and provides teams a framework to conduct these essential activities.

DURHAM – Researchers at Duke University and the University of Maryland have used the frequency of measurements on a quantum computer to get a glimpse into the quantum phenomena of phase changes – something analogous to water turning to steam. By measuring the number of operations that can be implemented on a quantum computing system without triggering the collapse of its quantum state, the researchers gained insight into how other systems -- both natural and computational -- meet their tipping points between phases. The results also provide guidance for computer scientists working to implement quantum error correction that will eventually enable quantum computers to achieve their full potential. The results appeared online June 3 in the journal Nature Physics. When heating water to a boil, the movement of molecules evolves as the temperature changes until it hits a critical point when it starts to turn to steam.

Researchers at Duke University have demonstrated that incorporating known physics into machine learning algorithms can help the inscrutable black boxes attain new levels of transparency and insight into material properties. In one of the first projects of its kind, researchers constructed a modern machine learning algorithm to determine the properties of a class of engineered materials known as metamaterials and to predict how they interact with electromagnetic fields. Because it first had to consider the metamaterial's known physical constraints, the program was essentially forced to show its work. Not only did the approach allow the algorithm to accurately predict the metamaterial's properties, it did so more efficiently than previous methods while providing new insights. The results appear online the week of May 9 in the journal Advanced Optical Materials.

In her 2018 book "Double Negative: The Black Image and Popular Culture," Racquel Gates explores the disruptive potential of stereotypical or so-called negative images of Black people onscreen: Flavor Flav on VH1's "Flavor of Love," for example, and the stars of "ratchet" reality shows such as "Basketball Wives." These images, Gates argues, intervene against narratives of racial uplift that are overly tethered to white and middle-class definitions of respectability. In her acknowledgments section, Gates, a professor of film and media studies at Columbia, invokes a scene from "Love & Hip Hop," in which an aspiring singer tells an entertainment manager, "I want to be on your roster." Gates writes, "While I was tempted to quote this bit of dialogue to my editor, Ken Wissoker, during our first meeting, I erred on the side of caution." Wissoker, who has been an editor at Duke University Press since 1991, has a formidable roster, and one could easily imagine a reality show about junior scholars fighting for a chance to work with him.

The novel computer software looks at retinal structure and blood vessels on images of the inside of the eye that have been correlated with cognitive changes. The findings, appearing last week in the British Journal of Ophthalmology, provide proof-of-concept that machine learning analysis of certain types of retinal images has the potential to offer a non-invasive way to detect Alzheimer's disease in symptomatic individuals. "Diagnosing Alzheimer's disease often relies on symptoms and cognitive testing," said senior author Sharon Fekrat, M.D., retina specialist at the Duke Eye Center. "Additional tests to confirm the diagnosis are invasive, expensive, and carry some risk. Having a more accessible method to identify Alzheimer's could help patients in many ways, including improving diagnostic precision, allowing entry into clinical trials earlier in the disease course, and planning for necessary lifestyle adjustments."